RewardDS introduces a novel framework that enables fine-tuning large language models while preserving privacy in sensitive domains.

• Uses reward-driven data synthesis to replace private data with high-quality synthetic alternatives
• Implements Differential Privacy (DP) guarantees for robust privacy protection
• Intelligently filters out flawed synthetic data to maximize model performance
• Demonstrates effectiveness in healthcare and financial applications

Why This Matters for Healthcare: Medical institutions can now fine-tune specialized LLMs without risking patient data exposure, enabling advanced AI applications while maintaining strict privacy compliance and patient trust.

RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis